Example:<\/strong><\/p>\n\n\n\nimport pandas as pd\ndf = pd.DataFrame()\ndf<\/pre>\n\n\n\nOutput:<\/strong><\/p>\n\n\n\nEmpty DataFrameCreate a DataFrame from Lists.<\/strong><\/h3>\n\n\n\nThe DataFrame can be created using a single list or a list of lists.<\/p>\n\n\n\n
Example:<\/strong><\/p>\n\n\n\nimport pandas as pd\ndata = [['Alex',10],['Bob',12],['Clarke',13]]\ndf = pd.DataFrame(data,columns=['Name','Age'])\nprint df<\/pre>\n\n\n\nOutput:<\/strong><\/p>\n\n\n\n Name AgeCreate a DataFrame from Dict of ndarrays \/ Lists<\/strong><\/p>\n\n\n\nAll the ndarrays must be of the same length. If the index is passed, then the length of the index should equal the length of the arrays.<\/p>\n\n\n\n
If no index is passed, then by default, the index will be a range(n), where n is the array length.<\/p>\n\n\n\n
Example:<\/strong><\/p>\n\n\n\nimport pandas as pd\ndata = {'Name':['Tom', 'Jack', 'Steve', 'Ricky'],'Age':[28,34,29,42]}\ndf = pd.DataFrame(data, index=['rank1','rank2','rank3','rank4'])\nprint df\n<\/pre>\n\n\n\nOutput:<\/strong><\/p>\n\n\n\n Age NameCreate a DataFrame from List of Dicts<\/strong><\/p>\n\n\n\nA list of Dictionaries can be passed as input data to create a DataFrame. The dictionary keys are by default taken as column names.<\/p>\n\n\n\n
The following example shows how to create a DataFrame by passing a list of dictionaries.<\/p>\n\n\n\n
Example:<\/strong><\/p>\n\n\n\nimport pandas as pd\ndata = [{'a': 1, 'b': 2},{'a': 5, 'b': 10, 'c': 20}]\ndf = pd.DataFrame(data)\nprint df<\/pre>\n\n\n\nOutput<\/strong> :<\/p>\n\n\n\n a b cObserve, NaN (Not a Number) is appended in missing areas.<\/p>\n\n\n\n
How to select a specific column from the DataFrame<\/strong><\/p>\n\n\n\nConsider the following DataFrame<\/p>\n\n\n\nAge Name SexNow we can access a specific column from the DataFrame by simply writing like this df[\u2018Name\u2019] <\/code> And the resultant DataFrame is displayed<\/p>\n\n\n\nName How To Select an Index or Column From a Pandas DataFrame<\/strong><\/p>\n\n\n\nIn-order to select rows in a DataFrame we will use the following functions<\/p>\n\n\n\n
iloc[]<\/li> loc[]<\/li><\/ul>\n\n\n\nExample:<\/strong><\/p>\n\n\n\none three two.iloc[] function is used to get rows (or columns) at particular positions in the<\/strong> indexdf.iloc[0:2]<\/code><\/p>\n\n\n\none three two.loc[] function is used to get rows (or columns) with particular labels from the indexdf.loc[\u2018a\u2019]<\/code><\/p>\n\n\n\none 1.0How To Add an Index, Row or Column to a Pandas DataFrame<\/strong><\/p>\n\n\n\nNow that you have learned how to select a value from a DataFrame, it\u2019s time to get to the real work and add an index, row, or column to it!<\/p>\n\n\n\n
Add New Column to DataFrame<\/strong><\/p>\n\n\n\nConsider a New Dataframe with Sales data from three different regions. We have data from the following region: West, North, and South.<\/p>\n\n\n\nRegion Company Product Month SalePandas allow you to add a new column Purchase\u2019s to this DataFrame<\/p>\n\n\n\npurchase = [3000, 4000] And this is the resultant DataFrame<\/p>\n\n\n\nRegion Company Product Month Sale PurchaseAdd New Row to DataFrame<\/strong><\/p>\n\n\n\nThis is a data dictionary with the values of one Region – East that we want to enter in the above dataframe. The data is basically a list with Dictionary having columns as key and their corresponding values.<\/p>\n\n\n\n
df=[{\u2018Region\u2019:\u2019South\u2019,\u2019Company':'D_Mart','Product': \n'Tables','Month':'December','Sales': 1500, 'Purchase':3500}]<\/pre>\n\n\n\nAnd this is the resultant DataFrame<\/p>\n\n\n\nRegion Company Product Month Sale PurchaseAdd New Index to DataFrame<\/strong><\/p>\n\n\n\nThe index of a DataFrame is a set that consists of a label for each row<\/p>\n\n\n\n
Now we will consider this DataFrame, and try to set a column as an index.<\/p>\n\n\n\nRegion Company Product Month Sale PurchaseThis is how we do itdf = df.set_index(\u2018Region\u2019)<\/code><\/p>\n\n\n\nThis is how the DataFrame looks after setting \u201cRegion\u201d as an index<\/p>\n\n\n\nRegion Company Product Month Sale Purchase The column \u2019Region\u2019 is now the index of the DataFrame.<\/p>\n\n\n\n
Resetting the Index of Your DataFrame<\/strong><\/p>\n\n\n\nWhen your index doesn\u2019t look entirely the way you want it to, you can opt to reset it. You can easily do this with reset_index()<\/code><\/p>\n\n\n\nHow to Delete Rows or Columns From a Pandas Data Frame<\/strong><\/p>\n\n\n\nNow that you have seen how to select and add indices, rows, and columns to your DataFrame<\/p>\n\n\n\n
Consider a DataFrame<\/p>\n\n\n\nRegion Company Product Month Sale PurchaseDropping columns with the column name<\/strong><\/p>\n\n\n\nTo get rid of columns from your DataFrame, you can use the drop() method:df=df.drop(\u2018Purchase\u2019) <\/code><\/p>\n\n\n\nDropping Rows by index label<\/strong><\/p>\n\n\n\nTo get rid of row from your DataFrame, you can use the drop() method:df=df.drop(\u20180\u2019,inplace = True)<\/code><\/p>\n\n\n\nWe use ‘inplace<\/strong>=True<\/strong>‘ if we want to commit the changes to the dataframe<\/p>\n\n\n\nWe will discuss the rest of the Pandas functions and other features in the next article.<\/p>\n\n\n\n
<\/p>\n","protected":false},"excerpt":{"rendered":"
Pandas is a popular Python package for data science, and with good reason, it offers powerful, expressive, and flexible data structures that make data manipulation and analysis easy, among many other things. The DataFrame is one of these structures. What is Pandas ? Pandas is a Python package providing fast, flexible, and expressive data structures […]<\/p>\n","protected":false},"author":1,"featured_media":4783,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[500],"tags":[1337,1336],"class_list":["post-4757","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-data-science-using-python-tutorials","tag-pandas","tag-pandas-data-structures"],"_links":{"self":[{"href":"https:\/\/www.h2kinfosys.com\/blog\/wp-json\/wp\/v2\/posts\/4757","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/www.h2kinfosys.com\/blog\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/www.h2kinfosys.com\/blog\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/www.h2kinfosys.com\/blog\/wp-json\/wp\/v2\/users\/1"}],"replies":[{"embeddable":true,"href":"https:\/\/www.h2kinfosys.com\/blog\/wp-json\/wp\/v2\/comments?post=4757"}],"version-history":[{"count":0,"href":"https:\/\/www.h2kinfosys.com\/blog\/wp-json\/wp\/v2\/posts\/4757\/revisions"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/www.h2kinfosys.com\/blog\/wp-json\/wp\/v2\/media\/4783"}],"wp:attachment":[{"href":"https:\/\/www.h2kinfosys.com\/blog\/wp-json\/wp\/v2\/media?parent=4757"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/www.h2kinfosys.com\/blog\/wp-json\/wp\/v2\/categories?post=4757"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/www.h2kinfosys.com\/blog\/wp-json\/wp\/v2\/tags?post=4757"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}
![\"\"](\"https:\/\/lh5.googleusercontent.com\/XpctC3pM23vE80sIGLyFgkEyBGl5y9CwYcWXd5ONcHzs6YeVgzLeog9uJfRZvENRn4sx5pFK8Bw9Xh_2ZS6_xVwyLSopax43bXXSzWv5p1-41dKuP45WKK2KE-eBlmzGCuUFdRUT9sNKWHEhZQ\" "\\"\\"")
<\/p>\n\n\n\n